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/admittance, force control Experience with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g
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of competitive research proposals. You should have experience in the following areas: Applied Machine Learning for Autonomous Systems: Experience developing and deploying ML models for perception, prediction
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machine learning technologies in order to provide evidence-based decision support tools in near real time across a variety of thematic domains: disaster risk reduction, sustainable agri-food systems
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3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation, remote object ranging
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technologies (fiber-optic sensors, DIC), and computer science (machine learning tools) in collaboration with de department of Physics. The aim of the BriCE project is to develop a novel bridge monitoring
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Validation (Serval) research group and work on a research project related to the application of machine learning for official statistics. The subjects of the research are: AI-assisted Bond Issuance, Causal
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graphs and related structures, limit theorems, stochastic calculus and applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years of postdoctoral experience
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in wireless communications and networking Background in AI and machine learning is an advantage. Experience and skills Knowledge of random-access protocols (e.g. IEEE 802.11 family). Understanding